MinMaxScaler

class caf.cvt.layering.MinMaxScaler(feature_range=(0, 1), *, copy=True, clip=False)

Bases: OneToOneFeatureMixin, TransformerMixin, BaseEstimator

Transform features by scaling each feature to a given range.

This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

The transformation is given by:

X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min

where min, max = feature_range.

This transformation is often used as an alternative to zero mean, unit variance scaling.

MinMaxScaler doesn’t reduce the effect of outliers, but it linearly scales them down into a fixed range, where the largest occurring data point corresponds to the maximum value and the smallest one corresponds to the minimum value. For an example visualization, refer to Compare MinMaxScaler with other scalers.

Read more in the User Guide.

Parameters:

• feature_range (tuple (min, max), default=(0, 1)) – Desired range of transformed data.

• copy (bool, default=True) – Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).

• clip (bool, default=False) –

  Set to True to clip transformed values of held-out data to provided feature_range. Since this parameter will clip values, inverse_transform may not be able to restore the original data.

  Note

  Setting clip=True does not prevent feature drift (a distribution shift between training and test data). The transformed values are clipped to the feature_range, which helps avoid unintended behavior in models sensitive to out-of-range inputs (e.g. linear models). Use with care, as clipping can distort the distribution of test data.

  Added in version 0.24.

min_

Per feature adjustment for minimum. Equivalent to min - X.min(axis=0) * self.scale_

Type:

ndarray of shape (n_features,)

scale_

Per feature relative scaling of the data. Equivalent to (max - min) / (X.max(axis=0) - X.min(axis=0))

Added in version 0.17: scale_ attribute.

Type:

ndarray of shape (n_features,)

data_min_

Per feature minimum seen in the data

Added in version 0.17: data_min_

Type:

ndarray of shape (n_features,)

data_max_

Per feature maximum seen in the data

Added in version 0.17: data_max_

Type:

ndarray of shape (n_features,)

data_range_

Per feature range (data_max_ - data_min_) seen in the data

Added in version 0.17: data_range_

Type:

ndarray of shape (n_features,)

n_features_in_

Number of features seen during fit.

Added in version 0.24.

Type:

int

n_samples_seen_

The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across partial_fit calls.

Type:

int

feature_names_in_

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 1.0.

Type:

ndarray of shape (n_features_in_,)

See also

minmax_scale

Equivalent function without the estimator API.

Notes

NaNs are treated as missing values: disregarded in fit, and maintained in transform.

Examples

>>> from sklearn.preprocessing import MinMaxScaler
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler()
>>> print(scaler.data_max_)
[ 1. 18.]
>>> print(scaler.transform(data))
[[0.   0.  ]
 [0.25 0.25]
 [0.5  0.5 ]
 [1.   1.  ]]
>>> print(scaler.transform([[2, 2]]))
[[1.5 0. ]]

Methods

__init__([feature_range, copy, clip])
fit(X[, y])	Compute the minimum and maximum to be used for later scaling.
inverse_transform(X)	Undo the scaling of X according to feature_range.
partial_fit(X[, y])	Online computation of min and max on X for later scaling.
transform(X)	Scale features of X according to feature_range.